Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument
Error in assign(cacheKey, frame, .rs.CachedDataEnv) : 
  attempt to use zero-length variable name
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) : 
  invalid first argument
Error in assign(cacheKey, frame, .rs.CachedDataEnv) : 
  attempt to use zero-length variable name
mxn10_plot <- ggplot(data = mxn10, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxn10_axis_set$chr, 
                     breaks = mxn10_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxn10_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 10,000 Step 2,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn10_plot


mxn50_plot <- ggplot(data = mxn50, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxn50_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_plot


### Regions of Interest ###
## Looking at Chromosomes with Fst of 0.25 or greater ##
mxn50_chr1_plot <- ggplot(data = filter(mxn50, chr == "NC_055957.1"), 
                          mapping = aes(x = pos_cum, 
                                        y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr1_plot


mxn50_chr2_plot <- ggplot(data = filter(mxn50, chr == "NC_055958.1"), 
                          mapping = aes(x = pos_cum, 
                                        y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#869ca8") +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr2_plot


mxn50_chr3_plot <- ggplot(data = filter(mxn50, chr == "NC_055959.1"), 
                          mapping = aes(x = pos_cum, 
                                        y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr3_plot


mxn50_chr10_plot <- ggplot(data = filter(mxn50, chr == "NC_055966.1"), 
                           mapping = aes(x = pos_cum, 
                                         y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr10_plot


mxn50_chr12_plot <- ggplot(data = filter(mxn50, chr == "NC_055968.1"), 
                           mapping = aes(x = pos_cum, 
                                          y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr12_plot


### Chromosome 2 Spike Region ###

mxn50_spike_plot <- mxn50 %>%
  filter(chr == "NC_055958.1") %>%
  filter(midPos >= 16600000) %>%
  filter(midPos <= 17000000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#869ca8") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife Chromosome 2", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_spike_plot


### Chromosome 3 "spike" region ###
mxn50_chr3_spike_plot <- mxn50 %>%
  filter(chr == "NC_055959.1") %>%
  filter(midPos >= 24600000) %>%
  filter(midPos <= 24800000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#242b35") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "Southern vs Northern Anadromous Alewife Chromosome 3", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr3_spike_plot

mxg10_plot <- ggplot(data = mxg10, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxg10_axis_set$chr, 
                     breaks = mxg10_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxg10_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 10,000 Step 2,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg10_plot


mxg50_plot <- ggplot(data = mxg50, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxg50_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_plot


### Regions of Interest ###
## Looking at the same chromosomes for MIDA vs NATLA ##
mxg50_chr1_plot <- ggplot(data = filter(mxg50, chr == "NC_055957.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr1_plot


mxg50_chr2_plot <- ggplot(data = filter(mxg50, chr == "NC_055958.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#869ca8") +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr2_plot


mxg50_chr3_plot <- ggplot(data = filter(mxg50, chr == "NC_055959.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr3_plot


mxg50_chr10_plot <- ggplot(data = filter(mxg50, chr == "NC_055966.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr10_plot


mxg50_chr12_plot <- ggplot(data = filter(mxg50, chr == "NC_055968.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr12_plot


### Chromosome 2 Spike Region ###

mxg50_spike_plot <- mxg50 %>%
  filter(chr == "NC_055958.1") %>%
  filter(midPos >= 16600000) %>%
  filter(midPos <= 17000000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#869ca8") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife Chromosome 2", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_spike_plot


### Chromosome 3 "spike" region ###
mxg50_chr3_spike_plot <- mxg50 %>%
  filter(chr == "NC_055959.1") %>%
  filter(midPos >= 24600000) %>%
  filter(midPos <= 24800000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#242b35") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "Southern vs Northern Anadromous Alewife Chromosome 3", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr3_spike_plot

nxg10_plot <- ggplot(data = nxg10, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = nxg10_axis_set$chr, 
                     breaks = nxg10_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(nxg10_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 10,000 Step 2,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg10_plot


nxg50_plot <- ggplot(data = nxg50, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(nxg50_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_plot


### Regions of Interest ###
## Looking at the same chromosomes for MIDA vs NATLA ##
nxg50_chr1_plot <- ggplot(data = filter(nxg50, chr == "NC_055957.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr1_plot


nxg50_chr2_plot <- ggplot(data = filter(nxg50, chr == "NC_055958.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#869ca8") +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr2_plot


nxg50_chr3_plot <- ggplot(data = filter(nxg50, chr == "NC_055959.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr3_plot


nxg50_chr10_plot <- ggplot(data = filter(nxg50, chr == "NC_055966.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr10_plot


nxg50_chr12_plot <- ggplot(data = filter(nxg50, chr == "NC_055968.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr12_plot


### Chromosome 2 Spike Region ###

nxg50_spike_plot <- nxg50 %>%
  filter(chr == "NC_055958.1") %>%
  filter(midPos >= 16600000) %>%
  filter(midPos <= 17000000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#869ca8") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife Chromosome 2", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_spike_plot


### Chromosome 3 "spike" region ###
nxg50_chr3_spike_plot <- nxg50 %>%
  filter(chr == "NC_055959.1") %>%
  filter(midPos >= 24600000) %>%
  filter(midPos <= 24800000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#242b35") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "N. Atlantic vs. Great Lakes Landlocked Alewife Chromosome 3", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr3_spike_plot

mxf10_plot <- ggplot(data = mxf10, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxf10_axis_set$chr, 
                     breaks = mxf10_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxf10_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 10,000 Step 2,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf10_plot


mxf50_plot <- ggplot(data = mxf50, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxf50_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_plot


### Regions of Interest ###
## Looking at the same chromosomes for MIDA vs NATLA ##
mxf50_chr1_plot <- ggplot(data = filter(mxf50, chr == "NC_055957.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_chr1_plot


mxf50_chr2_plot <- ggplot(data = filter(mxf50, chr == "NC_055958.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#869ca8") +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_chr2_plot


mxf50_chr3_plot <- ggplot(data = filter(mxf50, chr == "NC_055959.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_chr3_plot


mxf50_chr10_plot <- ggplot(data = filter(mxf50, chr == "NC_055966.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_chr10_plot


mxf50_chr12_plot <- ggplot(data = filter(mxf50, chr == "NC_055968.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_chr12_plot


### Chromosome 2 Spike Region ###

mxf50_spike_plot <- mxf50 %>%
  filter(chr == "NC_055958.1") %>%
  filter(midPos >= 16600000) %>%
  filter(midPos <= 17000000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#869ca8") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife Chromosome 2", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_spike_plot

nxf10_plot <- ggplot(data = nxf10, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = nxf10_axis_set$chr, 
                     breaks = nxf10_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(nxf10_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 10,000 Step 2,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf10_plot


nxf50_plot <- ggplot(data = nxf50, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(nxf50_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_plot


### Regions of Interest ###
## Looking at the same chromosomes for MIDA vs NATLA ##
nxf50_chr1_plot <- ggplot(data = filter(nxf50, chr == "NC_055957.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_chr1_plot


nxf50_chr2_plot <- ggplot(data = filter(nxf50, chr == "NC_055958.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#869ca8") +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_chr2_plot


nxf50_chr3_plot <- ggplot(data = filter(nxf50, chr == "NC_055959.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_chr3_plot


nxf50_chr10_plot <- ggplot(data = filter(nxf50, chr == "NC_055966.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_chr10_plot


nxf50_chr12_plot <- ggplot(data = filter(nxf50, chr == "NC_055968.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_chr12_plot


### Chromosome 2 Spike Region ###

nxf50_spike_plot <- nxf50 %>%
  filter(chr == "NC_055958.1") %>%
  filter(midPos >= 16600000) %>%
  filter(midPos <= 17000000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#869ca8") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife Chromosome 2", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_spike_plot

### Using ggsave to save the manhattan plots, or as I will now refer to it: ###
# Saving Private Plots (Ryan lol) ##
ggsave("figures/sliding-windows-fst/MIDA-x-NATLA-sz50-chrom2-spike.png", 
       plot = mxn50_spike_plot, 
       width = 10, 
       height = 4)
---
title: "Sliding Windows Analysis"
subtitle: "Alewife Populations of Interest Subset"
output: html_notebook
---

```{r libraries, echo = FALSE}
library(tidyverse)
library(ggtext)
```

```{r data_org, echo = FALSE}
cols <- c("region", 
          "chr", 
          "midPos", 
          "Nsites", 
          "Fst")


### Mid-Atlantic Anadromous Alewife vs Finger Lakes Landlocked Alewife ###
## Size 10,000 Step 2,000 ##
mxf10 <- read_delim("data/summarized/sliding_window_fst/MIDA--x--FINL--size-10000--step-2000.tsv", 
                    skip = 2, 
                    delim = "\t", 
                    col_names = cols,
                    show_col_types = FALSE)
mxf10_cum <- mxf10 %>%
  group_by(chr) %>%
  summarise(max_pos = max(midPos)) %>%
  mutate(pos_add = lag(cumsum(max_pos), default = 0)) %>%
  select(chr, pos_add)

mxf10 <- mxf10 %>%
  inner_join(mxf10_cum, by = "chr") %>%
  mutate(pos_cum = midPos + pos_add)

mxf10_axis_set <- mxf10 %>%
  group_by(chr) %>%
  summarise(center = mean(pos_cum))

## Size 50,000 Step 10,000 ##
mxf50 <- read_delim("data/summarized/sliding_window_fst/MIDA--x--FINL--size-50000--step-10000.tsv", 
                    skip = 2, 
                    delim = "\t", 
                    col_names = cols,
                    show_col_types = FALSE)
mxf50_cum <- mxf50 %>%
  group_by(chr) %>%
  summarise(max_pos = max(midPos)) %>%
  mutate(pos_add = lag(cumsum(max_pos), default = 0)) %>%
  select(chr, pos_add)

mxf50 <- mxf50 %>%
  inner_join(mxf50_cum, by = "chr") %>%
  mutate(pos_cum = midPos + pos_add)

mxf50_axis_set <- mxf50 %>%
  group_by(chr) %>%
  summarise(center = mean(pos_cum))


### Northern Atlantic Anadromous Alewife vs Finger Lakes Landlocked Alewife ###
## Size 10,000 Step 2,000 ##
nxf10 <- read_delim("data/summarized/sliding_window_fst/NATLA--x--FINL--size-10000--step-2000.tsv", 
                    skip = 2, 
                    delim = "\t", 
                    col_names = cols,
                    show_col_types = FALSE)
nxf10_cum <- nxf10 %>%
  group_by(chr) %>%
  summarise(max_pos = max(midPos)) %>%
  mutate(pos_add = lag(cumsum(max_pos), default = 0)) %>%
  select(chr, pos_add)

nxf10 <- nxf10 %>%
  inner_join(nxf10_cum, by = "chr") %>%
  mutate(pos_cum = midPos + pos_add)

nxf10_axis_set <- nxf10 %>%
  group_by(chr) %>%
  summarise(center = mean(pos_cum))

## Size 50,000 Step 10,000 ##
nxf50 <- read_delim("data/summarized/sliding_window_fst/NATLA--x--FINL--size-50000--step-10000.tsv", 
                    skip = 2, 
                    delim = "\t", 
                    col_names = cols,
                    show_col_types = FALSE)
nxf50_cum <- nxf50 %>%
  group_by(chr) %>%
  summarise(max_pos = max(midPos)) %>%
  mutate(pos_add = lag(cumsum(max_pos), default = 0)) %>%
  select(chr, pos_add)

nxf50 <- nxf50 %>%
  inner_join(nxf50_cum, by = "chr") %>%
  mutate(pos_cum = midPos + pos_add)

nxf50_axis_set <- nxf50 %>%
  group_by(chr) %>%
  summarise(center = mean(pos_cum))


### Northern Atlantic Anadromous Alewife vs Great Lakes Landlocked Alewife ###
## Size 10,000 Step 2,000 ##
nxg10 <- read_delim("data/summarized/sliding_window_fst/NATLA--x--GRTL--size-10000--step-2000.tsv", 
                    skip = 2, 
                    delim = "\t", 
                    col_names = cols,
                    show_col_types = FALSE)
nxg10_cum <- nxg10 %>%
  group_by(chr) %>%
  summarise(max_pos = max(midPos)) %>%
  mutate(pos_add = lag(cumsum(max_pos), default = 0)) %>%
  select(chr, pos_add)

nxg10 <- nxg10 %>%
  inner_join(nxg10_cum, by = "chr") %>%
  mutate(pos_cum = midPos + pos_add)

nxg10_axis_set <- nxg10 %>%
  group_by(chr) %>%
  summarise(center = mean(pos_cum))

## Size 50,000 Step 10,000 ##
nxg50 <- read_delim("data/summarized/sliding_window_fst/NATLA--x--GRTL--size-50000--step-10000.tsv", 
                    skip = 2, 
                    delim = "\t", 
                    col_names = cols,
                    show_col_types = FALSE)
nxg50_cum <- nxg50 %>%
  group_by(chr) %>%
  summarise(max_pos = max(midPos)) %>%
  mutate(pos_add = lag(cumsum(max_pos), default = 0)) %>%
  select(chr, pos_add)

nxg50 <- nxg50 %>%
  inner_join(nxg50_cum, by = "chr") %>%
  mutate(pos_cum = midPos + pos_add)

nxg50_axis_set <- nxg50 %>%
  group_by(chr) %>%
  summarise(center = mean(pos_cum))


### Mid-Atlantic Anadromous Alewife vs Great Lakes Landlocked Alewife ###
## Size 10,000 Step 2,000 ##
mxg10 <- read_delim("data/summarized/sliding_window_fst/MIDA--x--GRTL--size-10000--step-2000.tsv", 
                    skip = 2, 
                    delim = "\t", 
                    col_names = cols,
                    show_col_types = FALSE)
mxg10_cum <- mxg10 %>%
  group_by(chr) %>%
  summarise(max_pos = max(midPos)) %>%
  mutate(pos_add = lag(cumsum(max_pos), default = 0)) %>%
  select(chr, pos_add)

mxg10 <- mxg10 %>%
  inner_join(mxg10_cum, by = "chr") %>%
  mutate(pos_cum = midPos + pos_add)

mxg10_axis_set <- mxg10 %>%
  group_by(chr) %>%
  summarise(center = mean(pos_cum))

## Size 50,000 Step 10,000 ##
mxg50 <- read_delim("data/summarized/sliding_window_fst/MIDA--x--GRTL--size-50000--step-10000.tsv", 
                    skip = 2, 
                    delim = "\t", 
                    col_names = cols,
                    show_col_types = FALSE)
mxg50_cum <- mxg50 %>%
  group_by(chr) %>%
  summarise(max_pos = max(midPos)) %>%
  mutate(pos_add = lag(cumsum(max_pos), default = 0)) %>%
  select(chr, pos_add)

mxg50 <- mxg50 %>%
  inner_join(mxg50_cum, by = "chr") %>%
  mutate(pos_cum = midPos + pos_add)

mxg50_axis_set <- mxg50 %>%
  group_by(chr) %>%
  summarise(center = mean(pos_cum))


### Mid-Atlantic Anadromous Alewife vs Northern Atlantic Anadromous Alewife ###
## Size 10,000 Step 2,000 ##
mxn10 <- read_delim("data/summarized/sliding_window_fst/MIDA--x--NATLA--size-10000--step-2000.tsv", 
                    skip = 2, 
                    delim = "\t", 
                    col_names = cols,
                    show_col_types = FALSE)
mxn10_cum <- mxn10 %>%
  group_by(chr) %>%
  summarise(max_pos = max(midPos)) %>%
  mutate(pos_add = lag(cumsum(max_pos), default = 0)) %>%
  select(chr, pos_add)

mxn10 <- mxn10 %>%
  inner_join(mxn10_cum, by = "chr") %>%
  mutate(pos_cum = midPos + pos_add)

mxn10_axis_set <- mxn10 %>%
  group_by(chr) %>%
  summarise(center = mean(pos_cum))

## Size 50,000 Step 10,000 ##
mxn50 <- read_delim("data/summarized/sliding_window_fst/MIDA--x--NATLA--size-50000--step-10000.tsv", 
                    skip = 2, 
                    delim = "\t", 
                    col_names = cols,
                    show_col_types = FALSE)
mxn50_cum <- mxn50 %>%
  group_by(chr) %>%
  summarise(max_pos = max(midPos)) %>%
  mutate(pos_add = lag(cumsum(max_pos), default = 0)) %>%
  select(chr, pos_add)

mxn50 <- mxn50 %>%
  inner_join(mxn50_cum, by = "chr") %>%
  mutate(pos_cum = midPos + pos_add)

mxn50_axis_set <- mxn50 %>%
  group_by(chr) %>%
  summarise(center = mean(pos_cum))

```


```{r MIDA_x_NATLA_plots}
mxn10_plot <- ggplot(data = mxn10, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxn10_axis_set$chr, 
                     breaks = mxn10_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxn10_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 10,000 Step 2,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn10_plot

mxn50_plot <- ggplot(data = mxn50, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxn50_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_plot

### Regions of Interest ###
## Looking at Chromosomes with Fst of 0.25 or greater ##
mxn50_chr1_plot <- ggplot(data = filter(mxn50, chr == "NC_055957.1"), 
                          mapping = aes(x = pos_cum, 
                                        y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr1_plot

mxn50_chr2_plot <- ggplot(data = filter(mxn50, chr == "NC_055958.1"), 
                          mapping = aes(x = pos_cum, 
                                        y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#869ca8") +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr2_plot

mxn50_chr3_plot <- ggplot(data = filter(mxn50, chr == "NC_055959.1"), 
                          mapping = aes(x = pos_cum, 
                                        y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr3_plot

mxn50_chr10_plot <- ggplot(data = filter(mxn50, chr == "NC_055966.1"), 
                           mapping = aes(x = pos_cum, 
                                         y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr10_plot

mxn50_chr12_plot <- ggplot(data = filter(mxn50, chr == "NC_055968.1"), 
                           mapping = aes(x = pos_cum, 
                                          y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxn50_axis_set$chr, 
                     breaks = mxn50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr12_plot

### Chromosome 2 Spike Region ###

mxn50_spike_plot <- mxn50 %>%
  filter(chr == "NC_055958.1") %>%
  filter(midPos >= 16600000) %>%
  filter(midPos <= 17000000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#869ca8") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "Mid-Atlantic vs N. Atlantic Anadromous Alewife Chromosome 2", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_spike_plot

### Chromosome 3 "spike" region ###
mxn50_chr3_spike_plot <- mxn50 %>%
  filter(chr == "NC_055959.1") %>%
  filter(midPos >= 24600000) %>%
  filter(midPos <= 24800000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#242b35") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "Southern vs Northern Anadromous Alewife Chromosome 3", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxn50_chr3_spike_plot
```

```{r MIDA_x_GRTL_plots}
mxg10_plot <- ggplot(data = mxg10, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxg10_axis_set$chr, 
                     breaks = mxg10_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxg10_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 10,000 Step 2,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg10_plot

mxg50_plot <- ggplot(data = mxg50, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxg50_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_plot

### Regions of Interest ###
## Looking at the same chromosomes for MIDA vs NATLA ##
mxg50_chr1_plot <- ggplot(data = filter(mxg50, chr == "NC_055957.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr1_plot

mxg50_chr2_plot <- ggplot(data = filter(mxg50, chr == "NC_055958.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#869ca8") +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr2_plot

mxg50_chr3_plot <- ggplot(data = filter(mxg50, chr == "NC_055959.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr3_plot

mxg50_chr10_plot <- ggplot(data = filter(mxg50, chr == "NC_055966.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr10_plot

mxg50_chr12_plot <- ggplot(data = filter(mxg50, chr == "NC_055968.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxg50_axis_set$chr, 
                     breaks = mxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr12_plot

### Chromosome 2 Spike Region ###

mxg50_spike_plot <- mxg50 %>%
  filter(chr == "NC_055958.1") %>%
  filter(midPos >= 16600000) %>%
  filter(midPos <= 17000000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#869ca8") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Great Lakes Landlocked Alewife Chromosome 2", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_spike_plot

### Chromosome 3 "spike" region ###
mxg50_chr3_spike_plot <- mxg50 %>%
  filter(chr == "NC_055959.1") %>%
  filter(midPos >= 24600000) %>%
  filter(midPos <= 24800000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#242b35") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "Southern vs Northern Anadromous Alewife Chromosome 3", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxg50_chr3_spike_plot
```

```{r NATLA_x_GRTL_plots}
nxg10_plot <- ggplot(data = nxg10, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = nxg10_axis_set$chr, 
                     breaks = nxg10_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(nxg10_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 10,000 Step 2,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg10_plot

nxg50_plot <- ggplot(data = nxg50, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(nxg50_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_plot

### Regions of Interest ###
## Looking at the same chromosomes for MIDA vs NATLA ##
nxg50_chr1_plot <- ggplot(data = filter(nxg50, chr == "NC_055957.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr1_plot

nxg50_chr2_plot <- ggplot(data = filter(nxg50, chr == "NC_055958.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#869ca8") +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr2_plot

nxg50_chr3_plot <- ggplot(data = filter(nxg50, chr == "NC_055959.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr3_plot

nxg50_chr10_plot <- ggplot(data = filter(nxg50, chr == "NC_055966.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr10_plot

nxg50_chr12_plot <- ggplot(data = filter(nxg50, chr == "NC_055968.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxg50_axis_set$chr, 
                     breaks = nxg50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr12_plot

### Chromosome 2 Spike Region ###

nxg50_spike_plot <- nxg50 %>%
  filter(chr == "NC_055958.1") %>%
  filter(midPos >= 16600000) %>%
  filter(midPos <= 17000000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#869ca8") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Great Lakes Landlocked Alewife Chromosome 2", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_spike_plot

### Chromosome 3 "spike" region ###
nxg50_chr3_spike_plot <- nxg50 %>%
  filter(chr == "NC_055959.1") %>%
  filter(midPos >= 24600000) %>%
  filter(midPos <= 24800000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#242b35") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "N. Atlantic vs. Great Lakes Landlocked Alewife Chromosome 3", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxg50_chr3_spike_plot
```

```{r MIDA_x_FINL_plots}
mxf10_plot <- ggplot(data = mxf10, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxf10_axis_set$chr, 
                     breaks = mxf10_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxf10_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 10,000 Step 2,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf10_plot

mxf50_plot <- ggplot(data = mxf50, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(mxf50_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_plot

### Regions of Interest ###
## Looking at the same chromosomes for MIDA vs NATLA ##
mxf50_chr1_plot <- ggplot(data = filter(mxf50, chr == "NC_055957.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_chr1_plot

mxf50_chr2_plot <- ggplot(data = filter(mxf50, chr == "NC_055958.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#869ca8") +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_chr2_plot

mxf50_chr3_plot <- ggplot(data = filter(mxf50, chr == "NC_055959.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_chr3_plot

mxf50_chr10_plot <- ggplot(data = filter(mxf50, chr == "NC_055966.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_chr10_plot

mxf50_chr12_plot <- ggplot(data = filter(mxf50, chr == "NC_055968.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = mxf50_axis_set$chr, 
                     breaks = mxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_chr12_plot

### Chromosome 2 Spike Region ###

mxf50_spike_plot <- mxf50 %>%
  filter(chr == "NC_055958.1") %>%
  filter(midPos >= 16600000) %>%
  filter(midPos <= 17000000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#869ca8") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "Mid-Atlantic Anadromous vs Finger Lakes Landlocked Alewife Chromosome 2", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
mxf50_spike_plot
```

```{r NATLA_x_FINL_plots}
nxf10_plot <- ggplot(data = nxf10, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = nxf10_axis_set$chr, 
                     breaks = nxf10_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(nxf10_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 10,000 Step 2,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf10_plot

nxf50_plot <- ggplot(data = nxf50, 
                     mapping = aes(x = pos_cum, 
                                   y = Fst, 
                                   color = as_factor(chr))) +
  geom_point(alpha = 0.75, size = 0.5) +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_color_manual(values = rep(c("#242b35", "#869ca8"), 
                                  unique(length(nxf50_axis_set$chr)))) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_plot

### Regions of Interest ###
## Looking at the same chromosomes for MIDA vs NATLA ##
nxf50_chr1_plot <- ggplot(data = filter(nxf50, chr == "NC_055957.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_chr1_plot

nxf50_chr2_plot <- ggplot(data = filter(nxf50, chr == "NC_055958.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#869ca8") +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_chr2_plot

nxf50_chr3_plot <- ggplot(data = filter(nxf50, chr == "NC_055959.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_chr3_plot

nxf50_chr10_plot <- ggplot(data = filter(nxf50, chr == "NC_055966.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_chr10_plot

nxf50_chr12_plot <- ggplot(data = filter(nxf50, chr == "NC_055968.1"), 
                     mapping = aes(x = pos_cum, 
                                   y = Fst)) +
  geom_point(alpha = 0.75, size = 0.5, color = "#242b35") +
  scale_x_continuous(label = nxf50_axis_set$chr, 
                     breaks = nxf50_axis_set$center) +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  labs(x = NULL, 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_chr12_plot

### Chromosome 2 Spike Region ###

nxf50_spike_plot <- nxf50 %>%
  filter(chr == "NC_055958.1") %>%
  filter(midPos >= 16600000) %>%
  filter(midPos <= 17000000) %>%
  ggplot(.,
         mapping = aes(x = midPos, 
                       y = Fst, 
                       size = Nsites)) +
  geom_point(alpha = 0.75, color = "#869ca8") +
  scale_y_continuous(expand = c(0,0), 
                     limits = c(-0.05, 1)) +
  scale_size_continuous(range = c(0.5, 3)) +
  labs(x = "Position", 
       y = "Fst", 
       title = "N. Atlantic Anadromous vs Finger Lakes Landlocked Alewife Chromosome 2", 
       subtitle = "Size 50,000 Step 10,000") +
  theme_bw() +
  theme(legend.position = "none",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.title.y = element_markdown(),
        axis.text.x = element_text(angle = 90,
                                  size = 8,
                                  vjust = 0.5))
nxf50_spike_plot
```


```{r saving_plots}
### Using ggsave to save the manhattan plots, or as I will now refer to it: ###
# Saving Private Plots (Ryan lol) ##
ggsave("figures/sliding-windows-fst/MIDA-x-NATLA-sz50-chrom2-spike.png", 
       plot = mxn50_spike_plot, 
       width = 10, 
       height = 4)
```

